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公开(公告)号：US11138517B2
公开(公告)日：2021-10-05
申请号：US15674910
申请日：2017-08-11
Applicant: Google Inc.
Inventor： Pannag Sanketi , Wolfgang Grieskamp , Daniel Ramage , Hrishikesh Aradhye , Shiyu Hu
Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and/or client-provided input data used to generate predictions/inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.
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公开(公告)号：US10769549B2
公开(公告)日：2020-09-08
申请号：US15357559
申请日：2016-11-21
Applicant: Google Inc.
Inventor： Keith Bonawitz , Daniel Ramage
Abstract: The present disclosure provides systems and methods for the management and/or evaluation of machine-learned models based on locally logged data. In one example, a user computing device can obtain a machine-learned model (e.g., from a server computing device) and can evaluate at least one performance metric for the machine-learned model. In particular, the at least one performance metric for the machine-learned model can be evaluated relative to data that is stored locally at the user computing device. The user computing device and/or the server computing device can determine whether to activate the machine-learned model on the user computing device based at least in part on the at least one performance metric. In another example, the user computing device can evaluate a plurality of machine-learned models against locally stored data. At least one of the models can be selected based on the evaluated performance metrics.
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公开(公告)号：US10136043B2
公开(公告)日：2018-11-20
申请号：US15707302
申请日：2017-09-18
Applicant: Google Inc.
Inventor： Ryan M. Rifkin , Daniel Ramage
Abstract: The present disclosure relates to a method for controlling a digital photography system. The method includes obtaining, by a device, image data and audio data. The method also includes identifying one or more objects in the image data and obtaining a transcription of the audio data. The method also includes controlling a future operation of the device based at least on the one or more objects identified in the image data, and the transcription of the audio data.
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Patent Agency Ranking
公开(公告)号：US20180144265A1
公开(公告)日：2018-05-24
申请号：US15357559
申请日：2016-11-21
Applicant: Google Inc.
Inventor： Keith Bonawitz , Daniel Ramage
Abstract: The present disclosure provides systems and methods for the management and/or evaluation of machine-learned models based on locally logged data. In one example, a user computing device can obtain a machine-learned model (e.g., from a server computing device) and can evaluate at least one performance metric for the machine-learned model. In particular, the at least one performance metric for the machine-learned model can be evaluated relative to data that is stored locally at the user computing device. The user computing device and/or the server computing device can determine whether to activate the machine-learned model on the user computing device based at least in part on the at least one performance metric. In another example, the user computing device can evaluate a plurality of machine-learned models against locally stored data. At least one of the models can be selected based on the evaluated performance metrics.
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15.发明授权公开(公告)号：US09836484B1
公开(公告)日：2017-12-05
申请号：US14984683
申请日：2015-12-30
Applicant: Google Inc.
Inventor： Iwona Bialynicka-Birula , Blaise Aguera-Arcas , Daniel Ramage , Hugh Brendan McMahan , Oliver Fritz Lange , Emily Anne Fortuna , Divya Tyamagundlu , Jess Holbrook , Kristine Kohlhepp , Juston Payne , Krzysztof Duleba , Benjamin Vanik , Alison Lentz , Jon Gabriel Clapper , Joshua Denali Lovejoy , Aaron Michael Donsbach
CPC classification number: G06F17/3028 , G06K9/00261 , G06K9/6262 , H04N1/2137 , H04N5/23216 , H04N5/23219 , H04N5/23241 , H04N5/23245 , H04N5/23293
Abstract: The present disclosure provides an image capture, curation, and editing system that includes a resource-efficient mobile image capture device that continuously captures images. The mobile image capture device is operable to input an image into at least one neural network and to receive at least one descriptor of the desirability of a scene depicted by the image as an output of the at least one neural network. The mobile image capture device is operable to determine, based at least in part on the at least one descriptor of the desirability of the scene of the image, whether to store a second copy of such image in a non-volatile memory of the mobile image capture device or to discard a first copy of such image from a temporary image buffer without storing the second copy of such image in the non-volatile memory.
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公开(公告)号：US09769367B2
公开(公告)日：2017-09-19
申请号：US15048360
申请日：2016-02-19
Applicant: Google Inc.
Inventor： Ryan M. Rifkin , Daniel Ramage
CPC classification number: H04N5/23203 , G06F3/00 , G06K9/00355 , G06K9/00919 , G10L15/22 , G10L15/26 , G10L2015/223 , H04N5/23219
Abstract: The present disclosure relates to a method for controlling a digital photography system. The method includes obtaining, by a device, image data and audio data. The method also includes identifying one or more objects in the image data and obtaining a transcription of the audio data. The method also includes controlling a future operation of the device based at least on the one or more objects identified in the image data, and the transcription of the audio data.
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公开(公告)号：US09507830B2
公开(公告)日：2016-11-29
申请号：US14206880
申请日：2014-03-12
Applicant: GOOGLE INC.
Inventor： Pi-Chuan Chang , Daniel Ramage
IPC: G06F17/30
CPC classification number: G06F17/3053 , G06F17/30699 , G06F17/30867 , G06F17/30876
Abstract: A system stores a table mapping users to attributes, and stores a second table mapping the users to products associated with a source domain. The system determines a set of top scoring products for each of the attributes, and creates, using the top scoring products, a model that is predictive of an activity in a target domain, the target domain being separate from the source domain. The system detects a behavior from a particular user accessing the target domain, and generates a personalized prediction for the particular user based on the model, in response to the detecting the behavior.
Abstract translation: 系统存储将用户映射到属性的表，并存储将用户映射到与源域相关联的产品的第二表。 系统为每个属性确定一组最高评分产品，并使用最高评分产品创建一个预测目标域中活动的模型，目标域与源域分离。 系统检测来自访问目标域的特定用户的行为，并且响应于检测到行为而基于模型为特定用户生成个性化预测。
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公开(公告)号：US20160063393A1
公开(公告)日：2016-03-03
申请号：US14468710
申请日：2014-08-26
Applicant: Google Inc.
Inventor： Daniel Ramage , Jeremy Gillmor Kahn
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a global model for a particular activity, the global model derived based on input data representing multiple observations associated with the particular activity performed by a collection of users; determining, using the global model, expected data representing an expected observation associated with the particular activity performed by a particular user; receiving, by a computing device operated by the particular user, particular data representing an actual observation associated with the particular activity performed by the particular user; determining, by the computing device and using (i) the expected data and (ii) the particular data, residual data of the particular user; and deriving a local model of the particular user based on the residual data.
Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的用于获得用于特定活动的全局模型的计算机程序，所述全局模型基于表示与由用户集合执行的特定活动相关联的多个观察点的输入数据导出; 使用全局模型确定表示与由特定用户执行的特定活动相关联的预期观察值的预期数据; 由特定用户操作的计算设备接收表示与由特定用户执行的特定活动相关联的实际观察的特定数据; 由计算设备确定并使用（i）预期数据和（ii）特定数据，特定用户的剩余数据; 以及基于所述残差数据导出所述特定用户的本地模型。
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